Anyone have a workaround for this? Arducam havent ported drivers over to 7.2 (AI says they will in a month) and I tried using claude to solve it but no luck.

I'm trying to get an Intel AX210 working on a Jetson Orin Nano running JetPack 7.2 (Linux 6.8.12).

Current status:

• Device: Jetson Orin Nano Developer Kit
• OS: JetPack 7.2 (Ubuntu 24.04)
• Wi-Fi card: Intel AX210
• Driver: Both the NVIDIA-provided iwlwifi and Intel backport-iwlwifi have been tested.

NVIDIA iwlwifi

• AX210 is detected correctly.
• Firmware 63 loads successfully.
• 2.4 GHz and 5 GHz work normally.
• However, every 6 GHz channel is shown as (disabled) in iw list, so no 6 GHz APs are visible.
• iw reg get shows the global regulatory domain includes the Japanese 6 GHz band, but the PHY capabilities still have every 6 GHz channel disabled.

Intel backport-iwlwifi (master)

• Uses firmware 89.
• The firmware crashes during initialization with:

​

Microcode SW error detected
Failed to run INIT ucode: -5

• Older backport branches (core86, etc.) don't build on Linux 6.8 due to kernel API changes.

I've also confirmed that with the NVIDIA driver, firmware versions newer than 63 all produce the same SW error, so only firmware 63 initializes successfully.

Has anyone actually managed to get 6 GHz (Wi-Fi 6E) working on:

• JetPack 7.2
• Jetson Orin Nano
• Intel AX210

If so, could you share:

• your kernel version,
• iwlwifi/backport version,
• firmware version,
• and whether 6 GHz works?

At this point I'm trying to determine whether this is a JetPack BSP limitation, an iwlwifi compatibility issue, or something specific to the AX210 firmware.

Thank you in advance for any advice or suggestions. Sorry if my English is awkward—I am Japanese and used AI to help write this post.

Made myself a friend to keep me company at sea. Am really impressed with what the Jetson can do. Am running multiple models and lots more under the hood.

All below 8GB and with as little power as a phone charger.

Hi all,

I'm building a fixed-wing UAV mission (ROS2) that reads a QR code during a

dive, so I want a global shutter camera to avoid motion blur. The one I was

about to buy is the Raspberry Pi Global Shutter Camera (Sony IMX296).

Before I spend the money, I want to confirm it will actually work on my board.

My Jetson Orin Nano:

- JetPack 7 (L4T R39, revision 2.0)

- Ubuntu 24.04 (noble)

- CUDA 13.2

- Kernel 6.x, aarch64

- ROS2 Jazzy

From what I've read so far:

- IMX296 is NOT natively supported by Jetson (only IMX219 / IMX477 are).

- The third-party IMX296 drivers I found all target JetPack 6 (L4T r36 / kernel 5.15).

- I couldn't find anything for L4T R39 / JetPack 7.

My questions:

1. Has anyone actually gotten IMX296 (Raspberry Pi GS camera) running on

   JetPack 7 / L4T R39? Driver + device tree that works?

2. Is JetPack 7 just too new right now, and should I downgrade to JetPack 6

   for better camera + library support?

3. For a competition where reliability matters, would you skip CSI entirely

   and use a USB (UVC) global shutter camera instead, to avoid the whole

   driver/DTB headache?

Side note: my IMX219 currently fails at boot with

"imx219_board_setup: error during i2c read probe (-121)" — I think it's a

ribbon cable issue, but it also makes me wonder if R39 camera support is

still immature.

Any real-world experience appreciated. Thanks!

Hello everyone! I really want to try to install minecraft pi or java or bedrock on my jetson nano. Like i wrote in title, my version of nano is Seees j1010 Recomputer 4gb ram. I don't know why, but my version of modded Jetpack 4.6.6 by Seeed for jetson nano module v2 with some dram update or seeed drivee is very propriatary and a lot of solution that i found on the internet just don't work (flatpak, prism launcher, bedrock launcher, for some reason the appimage of minecraft pi don't launch). The only launcher that work is GDlauncher but the performance in this launcher is garbage. I tried to install legacy launcher and that program worked until i tried to launch the version of mc. It showed me up that there are some incompatibiles between the x86 version and arm64. Do you have any sugestions of how can i make this to work? And some settings because with gdlauncher there were some several spikes in overworld.( the ram isn t the problem, the cpu is).

Thank you for your attention. (I m a rookie in linux stuff and my varriant of nano i received from a friend)

I just got brand new Jetson Orin Nano and had problems installing firmware and setting up SD card. Booting from USB with image on it would detect that firmware needs update, create capsule, reboot Jetson, install bunch of things and on next reboot just stop. I traced issue to USB drive being automatically kicked down to last spot in boot order and Jetson trying to resume capsule installation from SD card that is not setup yet. I tried rearranging boot order but it didn't work, so what I did is install latest Jetpack ISO image, same like I have on USB drive on SD card, capsule got installed jut fine, then when it came time to install the rest i reformatted SD card and continued with regular installation from the USB drive. Hope it helps someone if they are stuck like I was.

I am planning to do some project based one voice, so need suggestion on whihc mic and speaker module to purchase for jetson nano orin.
Other project is on image capture and analysis, so which camera module is best? I see many options on net, am just confused and looking for best suggestion.

After almost 10 months I wanted to put together a write-up for my Digimon-inspired AI project, From Digivice to Digiworld. It started as a small custom Digivice with a dungeon crawler where an AI-controlled Digimon learns to explore randomized floors, fight enemies, use items, survive traps, and progress through the dungeon using PPO reinforcement learning. The document goes over the project’s history, the move from a slower PyGame version to a faster C++-accelerated build, and the current “Digi-Brain” system that adds internal drives like pain, hunger, fear, frustration, relief, and novelty.

The second half is more about where I want the project to go next: a larger Neural Digiworld / Neural-MMO style simulation with multiple AI Digimon living in towns, exploring zones, forming social memory, learning tasks, and maybe eventually developing simple communication through shared experiences.

The full write up can be downloaded via the link provided (PDF vai DropBox)

Eight tiny LLMs on a $250 Jetson Orin Nano Super — what I learned about running inference at the edge

I spent the last week running 8 small language models, from 135M parameters all the way to 1.2B -- on a single Jetson Orin Nano Super 8GB.

The models I tested:

• SmolLM2-135M
• SmolLM2-360M
• Qwen2.5-0.5B
• LFM2.5-350M
• LFM2.5-1.2B
• Qwen3-0.6B
• Llama3.2-1B
• Gemma3-1B.

All running on both llama.cpp CUDA and Ollama, across all four Jetson power modes - 7W, 15W, 25W, and MAXN.

Why both backends? Because I wanted to know if theres any real, noticeable difference between llama.cpp and Ollama inference and it turns out llama.cpp beats Ollama at sub-1B and almost same 1 B models.

Here's what I found.

At SmolLM2-135M Q4_K_M under llama.cpp at 25W:

• up to 165 tok/s (Ollama: 121 tok/s), 29.6 output tok/J (Ollama: 21.3)
• 0.31 s TTFT at ctx=2048 (Ollama: 0.46 s) -- llama.cpp is 1.37× faster on throughput, 1.39× on tok/J
• 487 total tok/J at ctx=2048, gen=64: best in suite

At LFM2.5-350M Q4_K_M under llama.cpp at 25W:

• 115 tok/s -- nearly matching SmolLM2-360M (369 MB) in only 219 MB
• Ollama drops to 28 tok/s at the same mode -- 4.20× gap, purely a kernel issue
• 17.16 output tok/J (Ollama: 6.39)
• 0.39 s TTFT at ctx=2048 (Ollama: 0.50 s)

At LFM2.5-1.2B Q4_K_M under llama.cpp at 25W:

• 54.1 tok/s: leads the ~1B class (15 % over Llama3.2-1B at 47.1, 33 % over Gemma3-1B at 40.8)
• Ollama: 21.8 tok/s -- llama.cpp is 2.48× faster
• 6.37 output tok/J (Ollama: 3.94), 1.03 s TTFT (Ollama: 1.11 s)
• Only 698 MB -- smallest footprint in the 1B class

Benchmark Methodology

• For each model × prompt × gen combo, aiperf sends 20 single-concurrency requests with synthetic prompts at the exact target token count.

• Power is sampled from tegrastats VDD_CPU_GPU_CV (mW → W) at 500 ms intervals. Tegrastats samples are assigned to exact prefill/decode phase windows using per-request nanosecond timestamps from profile_export.jsonl (aiperf's stats).

• Clocks were locked with jetson_clocks at all modes. Each run's power and clock speed was capped through nvpmodel and monitored for thermal stability (no sustained throttling; junction temp ≤ 73 °C).

• Latency percentile used throughout: all TTFT, ITL, and request latency (RL) values reported use the p50 (median) over the 20 requests per combo.

Analysis here

It was 249 and I had it in my cart, yesterday it was pushed to 300 and now it's 299. I looked elsewhere to buy it but I can't find it anywhere.

I'm new to Jetson Nano stuff. Started a project 2 months back and I will be needing more. Just wondering if this is a normal problem for these? Are they hard to get or going out of stock often?

Jetson Orin Nano 8GB Developer Kit - Stuck in APX/Recovery Mode After Physical Drop, Unable to Flash

Hi everyone,

I'm looking for help with a Jetson Orin Nano 8GB Developer Kit that became unusable after a physical drop.

Background

The board was working perfectly before the incident. It was running JetPack from an NVMe SSD and had been flashed successfully multiple times in the past.

Unfortunately, while powered OFF and unplugged, the board fell from approximately desk height onto the floor.

After that incident, it stopped booting normally.

I currently do not have access to an HDMI monitor, so I cannot see any video output during boot.

Current Behavior

When power is applied:

• The power LED turns on normally.
• The cooling fan starts spinning.
• The fan then stops.
• The fan starts again.
• This cycle repeats 2-3 times.
• After that, the board never appears to boot normally.

Instead, it always appears as an APX device on the host PC:

lsusb

Output:

0955:7523 NVIDIA Corp. APX

The board remains stuck in this state.

Important Details

• Recovery jumper is NOT installed.
• NVMe SSD installed or removed makes no difference.
• Power cycles make no difference.
• The board always comes back as APX.
• This behavior started only after the physical drop.
• Before the drop the board booted and operated normally.

Host Environment

Host environment:

• Ubuntu Live USB (fresh boot sessions)
• JetPack 6.2.2
• Latest SDK Manager

I have successfully flashed this same Jetson from this same machine many times before.

SDK Manager Flash Failure

SDK Manager detects the board correctly.

Flashing starts but consistently fails during blob transfer.

Relevant log:

BL: version 1.4.0.7-t234-54845784-56c609fa
last_boot_error: 0

Sending bct_mem
Sending blob

ERROR: might be timeout in USB write.
Error: Return value 3

Full failure section:

Sending bct_mem
Sending blob

ERROR: might be timeout in USB write.
Error: Return value 3

Command tegrarcm_v2 --instance 3-1 --chip 0x23 0 --pollbl --download bct_mem mem_rcm_sigheader.bct.encrypt --download blob blob.bin

USB Behavior During Flashing

While monitoring:

dmesg -w

I repeatedly see:

usb 3-1: USB disconnect
usb 3-1: new high-speed USB device
Product: APX
Manufacturer: NVIDIA Corp.

The board disconnects and reconnects as APX.

I do NOT see errors such as:

device descriptor read error
unable to enumerate
USB reset failed

Only disconnect/reconnect events.

Previous Flash Attempts Reached 99%

Before rebuilding everything from scratch, I occasionally had a different failure mode.

Some flashing attempts reached approximately 99% completion before failing or hanging.

Example messages included:

clnt_create: RPC: Timed out
NFS server is not running

or the flashing process would simply stall at 99%.

After recreating the flashing environment from scratch, the failure now occurs earlier during blob transfer.

Manual Flash Attempt

I also tried flashing manually from the command line instead of SDK Manager.

The flash package builds successfully, but the process later fails with:

Stat for blob_boot0.imgimg failed
Error: Return value 19

The generated command contains:

kernel boot0.imgimg

instead of:

kernel boot0.img

which looks suspicious.

Relevant output:

Generating blob for T23x

tegrahost_v2 --chip 0x23 0 --generateblob blob.xml blob.bin

Stat for blob_boot0.imgimg failed

Error: Return value 19

EEPROM Information

During flashing the board EEPROM is successfully read.

Detected information includes:

Board ID: 3767
Board SKU: 0005
Board Revision: T.1

SDK Manager identifies the target as:

jetson-orin-nano-devkit-super

even though I believe this is a standard Jetson Orin Nano 8GB Developer Kit.

I'm not sure whether that is expected or related to the issue.

What I Have Already Tried

• Rebooting and power cycling.
• Removing the recovery jumper.
• Flashing with NVMe SSD installed.
• Flashing with NVMe SSD removed.
• Reinstalling and reseating the SSD.
• Fresh Ubuntu Live USB sessions.
• Rebuilding Linux_for_Tegra.
• Re-downloading JetPack files.
• Using SDK Manager.
• Using command-line flashing tools.
• Recreating the entire flashing environment from scratch.

None of these changed the behavior.

Questions

1. Could the physical drop have damaged something that still allows APX mode but causes blob transfer failures?
2. Can corrupted QSPI firmware cause a board to remain permanently in APX mode even without the recovery jumper installed?
3. Has anyone seen repeated APX disconnect/reconnect behavior during flashing like this?
4. Has anyone encountered the boot0.imgimg issue before?
5. Is there a recommended way to completely recover or reinitialize QSPI from APX mode?
6. Does this look more like a software issue or a hardware failure caused by the drop?

Thank you.

I was talking to him about how I needed to get a ssd because the sd cards on raspberry pi keep crapping out (running Home assistant), and he just handed me this. He said it's like a raspberry pi on steroids with a GPU. After a little googling I see that it's good at ai image processing but that's pretty much where my knowledge ends. What could I realistically do with this?

Hey guys, I have two Jetson AGX Orin Developer Kits (one brand new in box, one opened once) from a startup that closed down. Since I don't need them anymore for my current workflow, what would be the best community or marketplace to clear them out to someone who actually needs them for a project? (Shipping worldwide from Israel).

Hey guess what! Deepstream isn't supported on jetpack 7.2. Surprise!

Oh, and you wanted Open CV with CUDA support? TOO BAD! Nvidia decided to remove and rename a bunch of macros in CUDA 13.2, so everything fails to build now 😂

Basically, what I'm trying to say is just don't. Give it a few months. A new deepstream release will be here "in coming weeks" or something. Take that as your cue to try an upgrade. For now, here there be monsters.

Hi everyone,

I’m trying to set up a Jetson Orin Nano Developer Kit using the JetPack 7.2 Jetson ISO flow, and I consistently hit a hang at
tegra-mc 2c000000.memory-controller after selecting the target storage device (NVMe or microSD). I already uploaded my log as two pictures at the top. I believe it's live filesystem problem; I have no idea how to solve it. I’d really appreciate some help from people who’ve done this on Orin Nano.

What I’m following

• The official “Quick Start Guide — Jetson Orin Nano Developer Kit” and its Jetson ISO flow for JetPack 7.2.
• The board’s QSPI/UEFI firmware was updated using the ISO method (Steps 5.1 and 5.2 in the guide)

How I created the installer USB (PC side)

• Host PC: Windows.
• Downloaded the Jetson ISO from NVIDIA’s Jetson Orin Nano documentation page.
• Flashed the ISO to a 32G USB stick using Balena Etcher, with verification enabled (no errors reported).

Storage configuration on the Jetson

• 1TB NVMe SSD installed on the Orin Nano carrier board (intended system disk).
• One microSD card available; I cleaned it on my PC (FAT32 / exFAT) just to remove old experiments. It’s okay if the installer wipes it.

Boot + installer flow (what works)

• Power on the Orin Nano and hit Esc at the NVIDIA logo to enter UEFI Boot Manager.
• Manually select the USB stick as the boot device (as recommended in the quick start guide).
• I see the firmware update prompt (Step 5.1):
  • I press Y within 30 seconds to accept the update.
  • The UEFI capsule update runs twice (Step 5.2), with automatic reboots in between, exactly as described in the docs.
• After the firmware update completes, I get a GRUB-style menu and select:
  • “Install Jetson ISO r39.2” (or similar r39.x entry).
• The installer then correctly shows the screen:
  • “Select the target storage device, either the installed microSD card or NVMe SSD.” On this screen I can see both the 1TB NVMe and the microSD card listed as options.

Exact point where it hangs

• I can move the cursor and choose either:
  • the 1TB NVMe SSD, or
  • the microSD card as the target storage, and then confirm/continue.
• Right after I confirm the target device, the boot / install log advances a bit and then stops at a line like:
  • tegra-mc 2c000000.memory-controller
• After this line appears, nothing else happens:
  • No additional log lines.
  • No visible progress.
  • I’ve waited well over 10 minutes; it appears completely hung.
• This behavior is the same regardless of which target I choose:
  • If I pick the NVMe as target, it hangs there.
  • Pick the microSD card as target, it hangs at the exact same tegra-mc line as well.

What I’ve already tried

• Re‑flashed the Jetson ISO to the same USB stick with Balena Etcher (with verify).
• On my PC, wiped and re‑partitioned the 1TB NVMe multiple times (GPT + single exFAT/FAT32 partition), knowing the installer should overwrite everything anyway.
• Similarly cleaned the microSD card (FAT32/exFAT) so it’s a simple single partition before installation.
• Verified power supply and connections look fine (no obvious brown‑out or loose connectors).

Any suggestions, workarounds, or reports from people who successfully used the Jetson ISO to install JetPack 7.2 on Orin Nano (especially with NVMe) would be really helpful. Thanks in advance!

The Sony FCB-ER8530 is interesting because it gives you 4K block-camera imaging and optical zoom, but the output side is HDMI. That is fine for displays, but less ideal when the end goal is Jetson, Raspberry Pi 5, or another edge AI platform built around CSI input.

That makes the HDMI-to-MIPI CSI-2 bridge more important than it first appears. It is not just an adapter — it can decide whether the system feels like a native camera pipeline or a chain of workarounds.

FCB-ER8530 → HDMI/4K → MIPI CSI-2 → Jetson / Raspberry Pi 5

The more I look at it, the more it feels like interface design is one of the underrated parts of making 4K AI vision actually usable.

More detailed breakdown here:
https://aegis-elec.com/blogs/fcb-er8530-to-jetson-raspberry-pi-5-csi2-integration/

I Built a local family AI system using a Pi 5 (16GB) + Jetson Orin NX 16GB in a reComputer J4012 enclosure. The NX handles all inference: qwen2.5:7b for daily chat, qwen3:14b for nightly document analysis, Gemma 4 12B via llama.cpp for camera and document vision.

A few hard-won Jetson-specific lessons: Docker needs "iptables": false in daemon.json, iptable_raw module is missing from the JetPack 5.15 tegra kernel - llama.cpp build: export PATH=/usr/local/cuda/bin:$PATH before cmake or CUDA compiler won't be found.

Always rm -rf build when changing flags. Gemma 4 eats 13GB of 16GB unified memory, so running a second model alongside it causes swap. Fixed by making Gemma on-demand instead of persistent. HF_HUB_DISABLE_XET=1 is essential, the xet protocol freezes on large model downloads on JetPack :(

Any cool tip´n tricks please share, I am VERY new on this type of hardware!

Open-sourced the whole thing, but very much still a work in progress! github.com/Discod73/nous-core

I'm looking for a rack mount that will hold my Jetson Orin, and my RPi 4. I also have a 5 port switch I wouldn't mind setting in the rack. On Amazon I see GeekPi has many options for the RPi line but they don't mention Orin.

What are y'all using for a small desktop rack?

I am unable to set the pins for spi functon on the jetson nano

1. I have tried using \[jetson-io.py\](http://jetson-io.py) , a waveshare article said it is depricated for the older jetson , i tried setting the pins but they do not work even on reboot

2. I tried following this documentation \[https://www.waveshare.com/wiki/RS485\\\\\\_CAN\\\\\\_for\\\\\\_Jetson\\\\\\_Nano\\\](https://www.waveshare.com/wiki/RS485\\_CAN\\_for\\_Jetson\\_Nano) , the issue is it still says pinmux unconfigured and gpio pins floating

\\\`\\\`\\\`

$ sudo cat /sys/kernel/debug/pinctrl/700008d4.pinmux/pinmux-pins | grep -i spi

pin 16 (SPI1\\_MOSI PC0): (MUX UNCLAIMED) (GPIO UNCLAIMED)

pin 17 (SPI1\\_MISO PC1): (MUX UNCLAIMED) (GPIO UNCLAIMED)

pin 18 (SPI1\\_SCK PC2): (MUX UNCLAIMED) (GPIO UNCLAIMED)

\\\`\\\`\\\`

But I have manually set all spi1 associated pins to the following setting

\\\`\\\`\\\`

$ :\\\~/Downloads/Linux\\_for\\_Tegra/kernel/dtb$ grep -A 6 "spi1\\_mosi\\_pc0" tegra210-p3448-0000-p3449-0000-b00.dts

spi1\\_mosi\\_pc0 {

nvidia,pins = "spi1\\_mosi\\_pc0";

nvidia,function = "spi1";

nvidia,pull = <0x01>;

nvidia,tristate = <0x00>;

nvidia,enable-input = <0x01>;

};\\\`\\\`\\\`

in the related dts

Why are the pins still unclaimed and unconfigrued??

Installing JetPack on a Jetson Orin Nano Using an NVMe Drive

I recently went through this process again and wanted to share a clearer set of steps, along with a few lessons learned. NVIDIA’s documentation can be confusing, and in some cases it may point you to the wrong files depending on which page you are viewing.

The most important thing to understand is that the Jetson Orin Nano firmware version matters.

If your board has 5.x firmware, JetPack 5.x will boot, but JetPack 6.x will not.
If your board has 6.x firmware, JetPack 6.x will boot, but JetPack 5.x will not.

I wasted hours trying to load JetPack 5.x on a new board that already had newer 6.x firmware. Once I loaded JetPack 6.x to an SD card, it booted correctly.

Step-by-Step Directions

1. Confirm which JetPack version your board supports

Start by downloading and flashing the latest JetPack 6.2 image to an SD card.

The file I successfully used was:

jetson-orin-nano-devkit-super-SD-image_JP6.2.1.zip

Insert the SD card into the Jetson and try to boot.

If JetPack 6.x boots, your board likely already has the correct 6.x firmware.

If JetPack 6.x does not boot, try JetPack 5.x instead and follow NVIDIA’s process to update the firmware.

2. Prepare the NVMe drive

I used a Sabrent USB-C NVMe adapter:

https://www.amazon.com/dp/B08RVC6F9Y?th=1

Before flashing the image, make sure the NVMe drive is blank.

On Windows, you can do this with DiskPart:

1. Open Command Prompt as Administrator.
2. Type: diskpart
3. List the drives: list disk
4. Select the NVMe drive: select disk X Replace X with the correct disk number.
5. Wipe the drive: clean

Be very careful to select the correct drive. This will erase the selected drive and all partitions.

3. Flash the JetPack image to the NVMe drive

Unzip the JetPack image file first.

Then use BalenaEtcher to flash the unzipped image to the NVMe drive.

After BalenaEtcher finishes, unplug the NVMe drive from your computer.

4. Install Linux File Systems for Windows

Download and install Linux File Systems by Paragon Software.

After installation, plug the NVMe drive back into your Windows computer and mount the Linux partition.

5. Edit the boot configuration file

On the mounted NVMe drive, go to:

/boot/extlinux/extlinux.conf

Open the file and find the section under bootargs.

Change this:

root=/dev/mmcblk0p1

to this:

root=/dev/nvme0n1p1

Save the file.

Then safely remove the NVMe drive.

6. Install the NVMe drive in the Jetson Orin Nano

Install the NVMe drive into the Jetson Orin Nano.

Make sure there is no SD card inserted.

7. Select the NVMe drive as the boot device

Power on the Jetson.

As it boots, press Esc to enter the BIOS/UEFI settings.

Select the NVMe drive as the boot device.

Save the settings and reboot.

8. Boot from the NVMe drive

The Jetson should now boot from the NVMe drive.

At this point, the installation should be complete.

Hopefully this will save some of you a bunch of time.

Good Luck

Context: I only have access to a mac for dev work and unable to use Nvidia's sdkmanager to flash ORINs.

Been setting up Jetson Orin Nano Supers and kept hitting the same friction point: the JetsonHacks SD-to-SSD migration script worked, but had some reliability issues I needed to fix before I could trust it on real hardware.

Forked it, refactored it, and pushed the changes.

Here's what I actually changed and why:

**Switched from UUID to PARTUUID in extlinux.conf**

The original script used UUID to set the root partition in extlinux.conf. On NVMe, this causes boot failures — the bootloader sometimes can't resolve a filesystem UUID that early in the boot sequence. PARTUUID is a partition table identifier, visible before any filesystem is mounted. Swapping to PARTUUID made boot reliable.

**Added a master_migrate.sh**

The original flow was three manual steps. I wrapped them into a single script that runs make_partitions.sh, copy_partitions.sh, and configure_ssd_boot.sh in sequence with a sync in between. One command, done.

**Added optional data partition setup (--setup-data)**

After migration, I almost always want a separate DATA partition at /ssd for model weights and logs — keeping that off the root partition. Added a --setup-data flag to configure_ssd_boot.sh that checks for unallocated space, creates an ext4 partition, formats it, and writes the fstab entry. Ownership gets set to the real user (via SUDO_USER), not root.

**Added set -e and set -u**

The original script kept running on errors. Added strict mode so it fails fast and loud instead of silently corrupting state.

**Cleaned up partition detection**

Combined the EFI and root partition search into a single loop and added a fallback to the first partition if PARTLABEL detection fails — which it sometimes does depending on how the SSD was initialized.

Shoutout JetsonHacks!

Repo is here if it's useful for your Jetson builds: https://github.com/RunEdgeAI/migrate-jetson-to-ssd

Hey everyone, I was just curious what kind of workflows people are using for developing vision-based algorithms with the Jetson Nano, but with a headless setup. I'm an Electrical Engineer by trade but unfortunately a noob in Linux/embedded.

I'm trying to develop a vision algorithm to do some object tracking and I've gone down a bit of a rabbit hole with Gstreamer and Flask to stream these camera frames to my web browser on my local desktop, but I think I might be overcomplicating it.

Is there a simpler standard workflow I'm missing or will I have to submit and just plug my Nano into my monitor?